Connecting Deep Reinforcement Learning based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators

Deep Reinforcement Learning has emerged as an efficient dynamic obstacleavoidance method in highly dynamic environments . It has the potential toreplace overly conservative or inefficient navigation approaches . However, the integration of the method into existing navigation systems is still an open frontier due to the myopic nature of Deep Reinforcer Learning .…

RNN Transducer Models For Spoken Language Understanding

We present a comprehensive study on building and adapting RNN transducer(RNN-T) models for spoken language understanding . These end-to-end (E2E) models are constructed in three practical settings: a case where verbatimtranscripts are available, a constrained case where the only availableannotations are SLU labels and their values .…

Half Duplex Attack An Effectual Attack Modelling in D2D Communication

The visualization of future generation Wireless Communication Network WCN is the presumption of onward innovations, the fulfillment of userdemands in the form of high data rates, energy efficiency, low latency, and long-range services . In comparison to previous technologies, these technologies exhibit flat architecture, the involvement of clouds in the network, centralized architecture incorporating small cells which creates vulnerablebreaches initiating menaces to the security of the network .…

Unitary Subgroup Testing

We present a novel structural property of Clifford unitaries . Namely, that their (normalized) trace is bounded by $1/\sqrt{2$ in absolute value . We show a similar property for the $q$-aryCliffords . This allows us to analyze a simple single-query identity test under the Clifford promise and show that it has (at least) constant soundness .…

Auxiliary Tasks and Exploration Enable ObjectNav

ObjectGoal Navigation (ObjectNav) is an embodied task wherein agents are tonavigate to an object instance in an unseen environment . Agents achieve 24.5% success and 8.1% SPL, a 37% and 8% improvement over prior state-of-the-art, respectively, on the HabitatObjectNav Challenge .…

AR Based Half Duplex Attack in Beyond 5G networks

With the evolution of WCN (Wireless communication networks), the absolutefulfillment of security occupies the fundamental concern . In view of security, we have identified another research direction based on the attenuation impact of rain in WCN . An approach is initiated by an eavesdropper in which a securecommunication environment is degraded by generating Artificial Rain (AR), which creates an abatement in the secrecy rate, and the cybersecurity getscompromised .…

The virtual element method for the coupled system of magneto hydrodynamics

Virtual Element Method allows us to construct noveldiscretizations for simulating realistic phenomenon in magneto-hydrodynamics . We show that this VEM approximation will yield divergence freediscrete magnetic fields, an important property in any simulation in MHD . We present a model for magneticreconnection in a mesh that includes a series of hanging nodes, which we use tocalibrate the resolution of the method .…

WNARS WFST based Non autoregressive Streaming End to End Speech Recognition

Attention-based encoder-decoder (AED) end-to-end (E2E) models havedrawn more and more attention in the field of automatic speech recognition . Autoregressive beam search decoding makes it inefficient for high-concurrency applications . WNARS achieves a charactererror rate of 5.22% with 640ms latency, to the best of our knowledge, which is the state-of-the-art performance for online ASR .…

Image based Virtual Fitting Room

Virtual fitting room is a challenging task yet useful feature for e-commerce platforms and fashion designers . Existing works can only detect very few types of fashion items . We firstly used Mask R-CNN to find the regions of different fashion items, and secondly used Neural Style Transfer to change the style of the selected fashion items.…

Sound Probabilistic Inference via Guide Types

Probabilistic programming languages aim to describe and automate Bayesianmodeling and inference . Modern languages support programmable inference, which allows users to customize inference algorithms by incorporating guide programs . For Bayesian inference to be sound, guide programs must be compatible with model programs .…

COVID 19 Named Entity Recognition for Vietnamese

The current COVID-19 pandemic has lead to the creation of many corpora thatfacilitate NLP research and downstream applications to help fight the pandemic . Most of these corpora are exclusively for English . In this paper, we present the first manually-annotated COVI-19 domain-specific dataset for Vietnamese.…

The Single Noun Prior for Image Clustering

Self-supervised clustering methods have achieved increasing accuracy in recent years but do not yet perform as well as supervised classification methods . We hypothesize that the performance gap is due to the difficulty of specifying, without supervision, which featurescorrespond to class differences that are semantic to humans .…

Enabling Cross Domain Communication How to Bridge the Gap between AI and HW Engineers

A key issue in system design is the lack of communication between hardware, software and domain expert . A HW/SWco-design process of (reconfigurable) neural accelerators, therefore, is animportant sub-problem towards a common co-design methodology . The ultimate challenge is to define the constraints for the design space exploration on the system level – a task which requires deep knowledge and understanding ofhardware architectures, mapping of workloads onto hardware and the application domain, e.g.…

Algorithmic Obfuscation for LDPC Decoders

The main idea of logic locking is toinsert a key-controlled block into a circuit to make the circuit functionincorrectly without right keys . In the case that the algorithmimplemented by the circuit is naturally fault-tolerant or self-correcting, existing logic-locking schemes do not affect the system performance much evenif wrong keys are used .…

Re designing cities with conditional adversarial networks

This paper introduces a conditional generative adversarial network to redesign a street-level image of urban scenes by generating an urban intervention policy, an attention map that localises where intervention is needed . The trained model shows strong performance in re-modellingcities, outperforming existing methods that apply image-to-image translation in other domains that is computed in a single GPU .…

On Biasing Transformer Attention Towards Monotonicity

Many sequence-to-sequence tasks in natural language processing are roughlymonotonic in the alignment between source and target sequence . In this work, we introduce amonotonicity loss function that is compatible with standard attentionmechanisms . Performance is mixed, with larger gains on top of RNN baselines .…

A Simple Geometric Method for Cross Lingual Linguistic Transformations with Pre trained Autoencoders

Powerful sentence encoders trained for multiple languages are on the rise . We investigate the use of a geometric mapping in embedding space to transformlinguistic properties without tuning of the pre-trained sentence encoderor decoder . We validate our approach on three linguistic properties using apre-trained multilingual autoencoder and analyze the results in bothmonolingual and cross-lingual settings .…

Towards End to End Neural Face Authentication in the Wild Quantifying and Compensating for Directional Lighting Effects

The recent availability of low-power neural accelerator hardware, combined with improvements in end-to-end neural facial recognition algorithms provides,enabling technology for on-device facial authentication . The present researchwork examines the effects of directional lighting on a State-of-Art(SoA) neuralface recognizer . Top lighting and its variants (top-left, top-right) arefound to have minimal effect on accuracy .…

Detection of Message Injection Attacks onto the CAN Bus using Similarity of Successive Messages Sequence Graphs

An attacker can inject messages (e.g.,increase the speed) that may impact the safety of the driver . This paper proposes a messageinjection attack detection mechanism independent of the IDs of the ECUs . The detection accuracy of the methods using a dataset collected from a moving vehicle undermalicious RPM and speed reading message injections show accuracy of98.45% when using LSTM-RNN .…

Learning What To Do by Simulating the Past

Recent work proposed that agents have access to a source of information that is effectively free: in any environment that humans have acted in, the state will already be optimized for human preferences . Such learning is possible in principle, but requires simulating all possible past trajectories that could have led to the observed state .…

Probing BERT in Hyperbolic Spaces

This work considers a family of geometrically special spaces, the hyperbolic spaces, that exhibit better inductive biases for hierarchical structures . We argue that a key desideratum of a probe is its sensitivity to the existence of linguistic structures . In a syntactic subspace, our probe recovers tree structures than Euclidean probes, revealing the possibility that the geometry of BERT syntax may not necessarily be Euclidan .…

Displacement Driven Approach to Nonlocal Elasticity

This study presents a physically consistent displacement-driven reformulation of the concept of action-at-a-distance, which is at the foundation of nonlocalelasticity . The (total) strain energy is guaranteed to be convex and positive-definite without imposing any constrainton the symmetry of the kernels .…

Learning to Coordinate via Multiple Graph Neural Networks

MGAN for collaborative multi-agentreinforcement learning is a new algorithm that combines graph convolutionalnetworks and value-decomposition methods . MGAN learns the representation of agents from different perspectives through multiple graph networks, and realizes the proper allocation of attention between all agents .…

Advances in Metric Ramsey Theory and its Applications

Metric Ramsey theory is concerned with finding large well-structured subsetsof more complex metric spaces . For finite metric spaces this problem was first studied by Bourgain, Figiel and Milman . In this paper we provide deterministicconstructions for this problem via a novel notion of \emph{metric Ramseydecomposition}.…